Sudden cardiac death (ASD) is a major public health problem affecting over 300,000 individuals in the United States each year. Over 5 million individuals in United States who have advanced left ventricular (LV) dysfunction, usually secondary to myocardial infarction or non-ischemic cardiomyopathy, are considered to be a high-risk of SCD. The current guidelines recommend a prophylactic placement of an implantable cardioverter defibrillator (ICD) in patients with a LV ejection fraction (LVEF) of <35%. The procedure itself is not curative, is associated with high costs, and is not free from short- and long-term adverse effects. Moreover, this preventive strategy, based on LVEF alone, results in 10 times more ICDs implanted than required to save a life, and in general reflects the limited understanding of the biologic pathways predisposing to ASD. In considering these risks, the impaired quality of life, and the increasing burden on the exiting resources due these expensive devices, there is a clear need for the development better risk-stratification techniques. As the currently known risk-factors for SCD are neither sensitive nor specific, this proposal is aimed at developing a novel biomarker panel that will help identify individuals who are at an increased risk of SCD, and thus the ideal candidates for ICD implantation. This proposal is an ancillary study within two of the largest, well-established, RO1-funded, and nationally representative cohort studies of patients undergoing ICD implantation - the PRospective Observational Study of the ICD in SCD (PROSE-ICD, n=1,200) and the Genetic Risk Assessment of Defibrillator Events (GRADE, n=2,000) studies. Both parent cohorts have remarkably similar enrollment criteria, patient characteristics, and availability of biologic material necessary for this study. The ancillary study will leverage the strengths of unbiased metabolome-wide scans, which include thousands of final downstream products of gene transcription, enzyme activity and metabolic products of extraneously administered substances, in order to identify a metabolomic fingerprint associated with at an increased risk of SCD. While the large sample size and the availability of independent discovery and validation cohorts will eliminate false positive associations, the availability of stringently phenotyped parent cohort will provide novel insights into the biologic and mechanistic pathways by which these metabolites may predispose to SCD. As the metabolome is the most proximal 'snapshot', the identification of the proposed metabolite panel will be a significant addition to the currently used risk-prediction algorithms for SCD and perfectly complement the overall specific aims of the parent cohorts. The results of this ancillary study will have direct implication over 5 million individuals who are considered ICD eligible and some findings may be applicable to the general population where majority of SCDs occur.